1. Field of the Invention
The present invention relates to pattern recognition, and particularly, though not exclusively, to speech recognition.
2. Description of the Related Art
The n-tuple method of pattern recognition, which was originally suggested by Bledsoe & Brown ("Pattern recognition and reading by machine", Proc. Eastern Joint Computer Conf., Boston, pp 225-232; 1959), has been proposed for the recognition of two-dimensional patterns. FIG. 1 shows an N.times.M pattern each element of which is represented by a single bit, either a "0" or a "1". Sets of n bits are selected in a specified way (e.g. at random) from the array forming in each case an `n-tuple`. Usually each bit is used once so that there are NM/n n-tuples. A template store (FIG. 2, which assumes n=4) has NM/n rows (one for each n-tuple) and 2.sup.n columns. In a training sequence each n-tuple is interpreted as a binary number from 0 to 2.sup.n -1 and a 1 is written into the corresponding column of the row assigned to that n-tuple. A number of training passes on patterns, all of course with the same n-tuple selection, will plot further 1's into the template store--which may or may not coincide with those already written in, according to the degree of similarity between the patterns.
Templates are formed in this way for a number of patterns to be recognised. When an unknown pattern is to be identified, n-tuples are formed in the same way and each is used to read out the corresponding location in one of the template stores. The number of `1`s found represents a `score` of the similarity between the unknown and the known pattern. Scores are obtained for each template and the unknown pattern is deemed to be recognised as being that corresponding to the template giving the highest score.
Tattersall and Johnston ("Speech Recognisers based on N-tuple Sampling", Proceedings of the Institute of Acoustics, Vol 6 part 4 pp 405-413, Autumn Conference, 1984) have proposed a speech recogniser using this principle. In this case, in the pattern of figure 1, the column represents successive samples in time of a word of speech, and the bits within each column represent a binary coding (e.g., a bar code) of that sample (or optionally of extracted features). This pattern is then analysed in much the same way as described above.